Hello
everyone,
nice
to
meet
you.
I'm
Egon
Gross
from
Symrise,
Germany.
From
professional,
I'm
a
biotechnologist
and
I'm
looking
forward
for
my
presentation
for
you.
Hello
everyone.
I'm
Bill
Worley.
I
am
a
JMP
systems
engineer
working
out
of
the
chemical
territory
in
the
central
region
of
the
US.
Hi.
My
name
is
Peter
Hersh.
I'm
part
of
the
Global
Technical
Enablement
Team,
working
for
JMP
out
of
Denver,
Colorado.
Peter welcome
to
our
presentation,
Data-
driven
selection
as
a
First
Step
for
a
Fast
and
Future
-Proof
Process
Development
.
First
I
want
to
introduce
the
company
I'm
working
for.
We
are
located
in
Holzminden,
more
or
less
in
the
center
of
Germany,
and
there
are
two
sites
coming
from
our
history.
Globally
seen,
we
are
located
with
the
headquarters
in Holzminden
.
We
have
big
subsidiaries
in
Peterborough,
in
Sao
Paulo
and
in
Singapore,
and
there
are
quite
a
lot
of
facilities
in
France
also
coming
due
to
our
history.
Coming
to
the
history,
Symrise
was
created
in
2003
out
of
a
merger
from
Harmon
and
Rhymer,
which
was
founded
in
1874,
in
Dragoko,
which
is
the
other
side,
from
our
facility,
which
was
established
1990.
Over
the
years
there
have
been
quite
some
acquisitions
and
also
in
2014
the
acquisition
of
Diana,
which
is
mainly
located
in
France
because
that's
the
reason
why
there
are
so
many
different
research
and
development
hubs.
Our
main
products
come
from
agricultural
products
or
from
chemical
processes
and
there
are
quite
a
lot
of
diverse
production
capacities,
production
possibilities
for
our
main
customers
being
human
or
pet.
As
is
so
diverse,
we
are
dealing
for
food
for
the
human
consumption,
for
pets
consumption,
and
also
for
health
benefits.
On
the
other
side,
the
segment
Scent
and
Care
is
dealing
with
fragrances
coming
from
fine
fragrances,
to
household
care,
to
laundry
,
whatever
thing
you
can
imagine,
that
smell
nicely.
As
I
said
in
the
beginning,
I'm
a
biotechnologist
by
training
and
I'm
dealing
a
lot
with
fermentation
processes
to
optimize
them
and
to
scale
them
up
or
down.
One
major
issue
when
it
comes
to
fermentation
is
the
broth,
the
liquid
composition
of
the
media,
which
will
then
feed
the
organisms.
No
matter
which
organisms
that
are,
they
need
carbon
sources,
they
need
nitrogen
sources,
they
need
minor
salts,
major
salts,
pH
values,
and
other
things.
It
is
often
important
which
kind
of
media
one
has.
When
it
comes
to
media
composition,
there
are
two
big
branches
which
can
be
seen.
One
is
the
branch
of
synthetic
media,
so
all
components
are
known
in
the
exact
amount
and
composition.
The
other
way
are
complex
media,
for
example,
having
a
yeast
extract
or
a
protein
extract
or
whatever,
where
it's
a
complex
mixture
of
different
substrates,
different
chemical
substances.
The
third
approach
would
be
a
mixture
of
both.
One
of
the
side
effects
of
these
complex
media
is
that
it's
quite
easy
to
deal
with
them.
But
on
the
other
hand,
there
can
be
constitutional
changes
over
time,
as
some
vendors
tends
to
optimize
their
processes,
their
products,
to
whatever
region,
to
whatever
target.
Some
customers
get
hold
of
those
changes,
some
don't.
Another
issue
might
be
the
availability o
f
it's
a
natural
product
like
[inaudible 00:04:38]
or
whatever.
You
might
know
some
ingredients,
you
will
surely
not
know
all
ingredients
and
there
might
be
promoting
or
inhibiting
substances
within
those
mixtures.
At
the
beginning
of
a
process
development,
the
media
is
of
main
importance.
Therefore
I
tried
to
look
at
carbon
sources,
nitrogen
sources,
salts,
trace
elements,
and
so
on,
being
my
different
raw
materials.
While
growing
the
organisms,
one
has
to
take
care
of
different
temperature,
stirring velocities
to
get
oxygen
into
the
liquids,
cultivation
time,
and
there
are
a
lot
of
unknown
variables
to
get
an
idea
what
the
effect
might
be
to
the
cell
dry
weight,
for
example,
or
to
the
different
targets
compounds
one
has
in
mind.
For
this
setup,
I
used
then
the
definitive
screening
design.
As
the
most
of
you
know,
they
are
quite
balanced
and
have
a
particular
shape
which
is
reflected
in
these
three-
dimensional
plot.
You
can
see
definitive
screening
design
is
somehow
walking
around
certain
edges
and
having
a
center
point.
Due
to
the
construction
of
the
definitive
screening
design,
one
can
estimate
interactions
and
square
effects.
These
interactions
are
not
confounded
with
the
main
factors
and
the
main
factors
itself
are
also
not
confounded
with
each
other.
This
is
a
very
nice
feature
of
the
definitive
screening
design
and
therefore
they
are
very
efficient
when
it
comes
to
the
workload
compared
to
formerly
known
screening
designs.
Some
disadvantages
are
also
there.
One
big
disadvantage
is
if
you
have
about
50%
of
the
factors
that
are
working
that
have
a
high
influence
or
even
more,
you
have
a
significant
influence,
significant
confounding,
which
you
have
to
take
care
of.
In
this
particular
case,
although
it's
the
leanest
possible
design
I
found,
the
practical
workload
would
require
five
to
six
months
just
for
screening.
This
is
far
too
long
when
it
comes
to
a
master
thesis.
The
alternative
was
then
to
build
another
design
or
to
build
another
process.
I
was
so
inspired
in
Copenhagen
2019
by
a
contribution
from
Bill
where
he
talked
about
infrared
spectroscopy
and
I
thought
why
that
might
be
a
good
idea,
using
the
chemical
information
hidden
in
a
near-infrared
spectrum
to
describe
the
chemical
composition
of
the
mixtures.
Therefore
I
established
this
workflow.
First,
the
media
preparation
was
done
of
all
the
65
mixtures.
Then
the
near-
infrared
spectrum
was
measured,
some
chemometric
treatments
were
preferred
and
afterwards,
the
space
of
variation
could
be
held
constant
at
a
maximum,
but
the
number
of
the
experiments
could
be
reduced
quite
significantly.
To
show
you
how
the
workflow
is,
I
started,
as
I
said,
with
spectral
information.
One
of
the
first
principles
one
has
to
do
is
to
make
a
standardization
of
the
spectra
to
avoid
baseline
shifts
and
things
like
that.
This
is
one
way
to
make
it.
Introducing
a
new
formula
to
standardize,
or
what
I
did,
I
used
an
add-in
to
preprocess
and
calculate
the
standard
normal
variety,
which
is
when
it
comes
to
the
digits,
the
same
as
the
standardization,
as
we
see
here.
With
this
standardized
spectra,
depending
on
each
measurement,
I
continued
then
and
compiled
first
all
these
spectra.
What
you
see
here
on
the
top
is
the
absorption
of
every
sample.
We
have
an
aqueous
system
so
we
took
water
as
a
reference.
A
fter
building
the
difference
between
the
absorption
and
the
water,
we
then
got
deeper
and
saw
differences
within
the
spectra.
One
of
the
big
question
was
do
I
calculate
first
the
difference
between
the
absorption
of
the
sample
and
the
water
and
calculate
then
the
standard
normal
variety?
Or
do
I
first
calculate
the
standardization
and
then
use
these
standardized
values
from
the
water
background?
One
could
think
the
procedure
is
the
same,
but
the
outcome
is
different.
As
you
see
here,
on
the
right-
hand
side
of
the
dashboard,
I
zoomed
into
this
area
and
in
the
lower
part,
the
curves
have
a
different
shape,
a
different
distance
from
each
other
than
in
the
upper
part.
T
his
might
have
then
an
influence
on
the
subsequent
regression
analysis.
Therefore,
I
selected
first
to
make
the
standardizing
and
then
the
difference
calculations.
After
I
did
these
first
steps,
then
came
the
chemometric
part,
that
is
smoothing
and
the
filtering and
to
calculate
the
derivatives.
This
is
a
standard
procedure
using
an
add-in
which
is
available.
You
can
imagine
that
the
signal
might
have
some
noise.
This
is
seen
here
in
the
lower
part,
the
red
area
is
the
real
data,
and
the
blue
curves
are
the
smooth
data.
On
the
left
upper
side,
you
see
the
first
derivative.
On
the
right
upper
side,
the
second
derivative
of
these
functions.
If
it
comes
to
polynomial
fits,
it's
depending
on
the
procedure,
what
you
are
fitting,
what's
the
polynomial
order,
and
how
broad
your
area
is,
where
you
make
the
calculations
in.
If
we
take
here
only
a
second-
order
polynomial,
you
see
that
it
might
change.
Now,
this
is
not
a
two,
this
ought
to
be
a
20.
Then
the
curve
smooths
out.
Although
it's
smooth,
you
can
see
differences
in
height,
in
shape.
To
get
hold
of
those
data,
one
has
to
save
the
smooth
data
to
data
tables,
separate
data
tables.
Then
I
tried
different
settings
for
the
smoothing
process,
because
I
did
not
know
from
the
beginning
which
process
is
the
best
to
fit
my
desired
outcome
of
the
experiment
at
the
end.
After
quite
a
lot
of
smoothing
tables,
which
were
then
manually
done,
and
I
then
concatenated
the
tables.
These
are
all
the
tables
we
just
made.
I'm
going
to
the
first
one
and
say,
please
concatenate
all
of
the
others.
The
nice
thing
is
that
you
then
have
at
the
end,
these
different
distances
coming
from
the
smoothing
effect.
I
had
a
second
polynomial
order.
A
third
polynomial
order
is
20
points
to
the
left
and
to
the
right
for
the
smoothing
process
and
30
and
so
on.
This
is
just
a
small
example
to
show
you
the
procedure.
I
did
quite
more.
What
I
did
was
this
amount
of
treatment.
I
had
[inaudible 00:15:01]
for
a
second,
third,
or
fifth
polynomial
order
with
10, 20,
or
30.
Now
came
the
big
part
to
decide
which
particular
procedure
represents
my
space
at
best.
This,
therefore,
I
made
a
principal
component
analysis
of
all
my
treatments
I
did.
This
is
a
general
overview.
The
numbers
represent
each
experiment
by
its
own
that
you
can
follow
them
in
the
different
scores
and
loading
spots.
The
loading
plot
isâ¦
That's
a
regular
picture
of
a
loading
plot
when
it
comes
to
spectral
data.
If
you
take
into
account
that
we
are
coming
from
a
design,
this
value
of
24%
explained
variation
at
the
beginning
for
the
first
component
is
very
high.
Why?
Because
the
factors
of
the
definitive
screening
designs
are
orthogonal
to
each
other
and
independent
from
each
other.
One
would
expect
lower
values
for
the
principal
components.
After
this
treatment,
the
first
derivative
with
second
order
polynomial
and
10
points
to
the
left
and
to
the
right
for
the
smoothing,
it
looks
very
evenly
distributed.
You
might
think
of
a
cluster
here
on
top
or
below.
I
went
through
all
of
these
particular
processes
and
selected
then
a
favored
one,
where
I
saw
that
the
first
principal
component
has
a
very
slow
describing
power
for
the
variation.
That's
then
the
way
I
proceeded.
After
selecting
the
particular
pre-
processed
data,
I
still
have
my
65
samples.
But
as
we
heard
at
the
beginning,
65
is
far
too
much
for
a
workload.
If
you
ask
yourself,
why
is
there
132
samples?
That
is
because
I
copy
pasted
the
design
below,
the
original
design
for
the
spectral
treatment
I
used
then.
If
you
want
then
to
select
your
runs
you
are
able
to
make
due
to
time
reasons
or
due
to
cost
reasons
or
whatever,
this
is
one
process
you
can
make
use
the
coverage
and
the
principal
components.
Then
this
is
the
design
space
which
is
available
dealing
for
the
all
variation
which
is
inside.
But
as
you
see,
we
would
need
to
make
132
experiments.
If
we
then
go
just
select
all
the
principal
components
and
say
please
make
only
the
one
which
are
possible,
then
you
have
the
ability
to
type
in
every
number
you
want
to.
At
this
stage,
I
selected
several
smaller
or
bigger
designs
and
saw
how
far
can
I
go
down
to
reach
at
least
a
good
description
power.
I
made
these
25
experiments,
let
JMP
select
them.
The
nice
thing
is
with
this
procedure,
if
you
are
coming
back
to
your
data
table,
they
are
selected.
But
this
procedure
I
didn't
do
right
at
the
beginning.
At
the
beginning,
I
made
a
manual
selection.
How
did
I
do
that?
I
took
the
score plot
of
the
particular
treatment
and
then
selected
manually
the
outer
points
as
good
as
possible.
Not
only
in
the
picture
of
the
first
and
second
principal
component,
but
I
went
deeper.
This,
for
example,
is
the
comparison
of
a
selection
method
I
just
showed
you
with
the
DOE of
the
constraint
factors
and
with
the
manual
selection,
just
for
showing
you
maybe
some
differences.
If
you
make
this
DOE selection
several
times,
don't
be
confused
to
get
not
always
the
same
numbers,
the
same
experiments,
which
might
be
important.
With
this
approach,
I
then
reduced
the
workload
from
64
experiments
to
25
experiments.
In
all
of
these
experiments,
all
my
raw
materials
I
had
from
the
beginning
were
inside.
I
didn't
leave
any
raw
material
out,
and
that
was
very
nice
to
see,
that
I
could
retain
the
space
of
the
variation.
After
the
cultivation
in
two
blocks,
which
took
a
frame
week
of
three
weeks
for
each
block,
we
yet
then
analyzed
our
metabolome
and
the
supernatant
and
determined
our
cell
drive
mass.
For
time's
sake,
I
show
you
only
the
results
and
the
procedure
for
the
cell
dry
mass.
Other
molecules
might
be
the
same
procedure
to
be
done
then.
The
next
issue
I
had
was
that
there
is
a
confounding.
I
had
to
expect
the
confounding
because
I
had
only
25
experiments
for
27
mixtures
coming
out
of
a
design
where
I
knew
where
I
supposed
to
have
interactions
and
quadratic
effects.
These
interactions
is
nothing
new
when
it
comes
to
media
composition.
Quadratic
effects
were
nice
to
be
seen.
Then
came
the
next
nice
thing,
which
was
introduced
by
Pete
Hersh
and
Phil
K.
It's
the
SVEM
process,
the
Self-V
alidated
Ensemble
Model.
In
this
sketch,
you
see
the
workflow
and
we
will
go
through
that
in
JMP.
The
first
thing
was
to
look
at
the
distribution
of
my
target
value.
After
making
a
log
transformation,
I
then
saw
that
it's
normally
distributed.
So
we
have
a
log-
normal
distribution.
That's
nice
to
know.
The
first
thing
was
to
download
this
add-
in,
Auto
validation
Set-up,
and
hit
the
run
button.
We
then
get
a
new
table.
The
new
table
has
50
rows
instead
of
25
rows
from
our
original
table.
Why
is
that
so?
The
reason
for
that
is
while
hitting
the
button,
the
data
table
gets
copy-
pasted
below
and
we
get
a
differentiation
into
the
validation
set
and
into
the
training
set,
as
you
see
here.
The
nice
feature
of
this
Auto
validation
table
is
that
you
can,
due
to
a
simulation,
find
out
which
parameters,
which
factors
have
an
influence.
This
happens
by
the
spared
fractionally
weighted
bootstrap
weight.
If
you
look
for
example,
the
second
experiment
has
a
value
of
1.8
in
the
training
set
and
the
same
sample
has
a
value
of
0.17
in
the
validation
set.
This
then
gives
one
the
ability
to
have
a
bigger
weight
for
some
samples
in
the
training
set
and
vice
versa
in
the
validation
set.
While
they
have
a
bigger
value,
a bigger
weight
in
the
training
set,
they
have
a
lower
weight
in
the
validation
set.
To
analyze
this,
it's
necessary
to
have
the
pro
version
to
make
a
generalized
regression.
As
we
took
the
log
value
of
our
cell
dry weight,
I
can
then
make
a
normal
distribution
and
then
it's
recommended
to
make
a
lasso
regression.
From
the
first
lasso
regression,
we
get
a
table
for
the
estimates,
and
now
comes
the
nice
part.
We
make
simulations
changing
the
paired
weight
bootstrap
weight
of
each
factor.
For
time's
sake,
I'm
just
making
50
simulations.
From
these
50
simulations,
we
get
then
the
proportion
for
each
factor
we
had
in
the
design
where
it
entered
the
regression
equation,
or
didn't
enter
the
regression
equation.
This
pattern
comes
due
to
this
randomization
process
of
the
bootstrap
forest
method.
From
this
distribution
we
go
to
the
summary
statistics,
customize
them,
we
are
just
only
interested
in
the
proportion
nonzero.
This
proportion
nonzero
is
finally
the
amount
of
the
50
simulations.
How
often
this
particular
variable
went
into
the
regression
equation.
From
this,
we
make
a
combined
data
table
and
have
a
look
on
the
percentage
of
each
variable
being
in
a
model
or
being
not
in
a
model.
This
looks
a
little
bit
confusing.
If
we
are
ordering
it
by
the
column
two
descending,
we
then
see
a
nice
pattern.
Now
you
can
imagine
why
I
introduced
at
the
beginning
this
null
factor
or
these
random
uniform
factors.
T
he
uniform
factors
were
manually
introduced.
The
null
factor
was
introduced
by
hitting
the
auto-
validation
set.
What
do these
points
mean?
These
points
mean
that
until
the
null
factor,
these
variables
have
a
high
potential
because
they
were
quite
often
within
the
model-
building
processes.
These
at
the
bottom
were
quite
seldom
within
the
model-
building
processes
so
the
ability
to
reduce
your
complexity
is
given
by
just
discarding
these.
Here
in
the
middle
one
has
to
decide
what
to
do.
After
having
this
extraction,
not
losing
information,
and
not
losing
variation,
one
can
then
think
of
different
regression
processes
making
response
surface
model
or
step
wise
regression
or
whatever
regression
you
have
in
mind.
It's
wise
to
compare
different
regression
models
looking
what's
feasible,
what's
meaningful.
That
was
the
procedure
I
used
in
JMP 16.
While
coming
now
to
Pete
and
Bill,
they
will
describe
you
something
else.
Thank
you,
Egon.
That
was
a
great
example
of
an
overview
of
your
workflow.
Thank
you.
What's
new
in
JMP 17
that
might
have
helped
Egon
a
little
bit
with
the
tools
he
was
working
with?
I'm
going
to
start
off
with
a
little
bit
of
a
slide
show
here.
I'm
going
to
be
talking
about
Functional Data Explorer.
That's
in
JMP Pro
and
talking
about
the
pre-
processing
and
Wavelet
modeling
that
are
built
into
Functional Data Explorer
now.
All
right,
so
let
me
slide
this
up
a
little
bit
so
you
can
see.
What's
new
in
JMP 17?
We've
added
some
tools
that
allow
for
a
better
chemometric
analysis
of
spectral
data.
Really
any
multivariate
data
that
you
might
have
that
you
can
think
of,
these
tools
are
there
to
help.
First
is
adding
the
preprocessing
methods
that
are
built
into
FDE
now.
We've
got
standard
normal
variant,
which
Egon
showed
you.
We've
got
multiplicative
scatter
correction,
which
is
a
little
bit
more
powerful
than
the
standard
normal
variant.
Both
of
these
will
not
disrupt
the
character
of
your
spectra.
That's
not
the
story
with
Savitzky-Golay.
It
does
alter
the
spectra,
which
will
then
make
a
little
bit
harder
to
interpret
the
data.
The
key
thing
is
it
still
helps.
Then
we
have
something
called
polynomial
baseline
correction,
which
is
another
added
tool
if
you
need
that.
The
next
step
would
be
then
to
save
that
preprocess
data
for
further
analysis,
like
principal
component
analysis,
partially
squares,
so
on
and
so
forth,
so
you
can
do
some
analysis
there.
The
Wavelet
modeling
is
a
way
to
look
at
the
chemometric
data
similar
to
principal
component
analysis.
We're
fitting
a
model
to
the
data
to
determine
which
is
the
best
overall
fit
for,
in
this
case,
25
spectra.
That's
the
goal
here.
It's
an
alternative
to
spline
models.
It's
typically
better
than
spline
models,
but
not
always.
You
get
to
model
the
whole
spectra,
not
the
point-
by-
point,
which
you
would
do
with
other
analysis
types.
Then
you
get
to
discern
these
things
called
shape
functions
that
make
up
the
curve.
These
shape
functions
are,
again,
similar
to
principal
component
analysis
in
that
they
are
helping
with
dimension
reduction.
Then,
as
I
said
before,
these
are
excellent
tools
for
spectral
and
chromatographic
data,
but
virtually
any
multivariate
data
is
fair
game.
These
are
just
an
example
of
the
Wavelet
functions
that
are
built
in.
I
could
try
and
pronounce
some
of
these
names,
but
I'll
mess
them
up,
but
know
that
these
are
in
there.
There
is
a
site
here
that
you
can
look
up
what
these
Wavelets
are
all
about.
I
got
the
slide
from
Ryan
Parker
so
thank
you,
Ryan.
Almost
last
but
not
least,
what
we're
doing
with
this
functional
principal
component
analysis
is
we're
trying
to
determine,
again,
what's
the
best
overall
fit
for
these
data
and
then
compare
the
curves
as
needed.
What
comes
out
of
the
Wavelet
modeling
is
a
Wavelet
DOE,
and
we
determine
which
wavelengths
have
the
highest
energy
for
any
given
spectra
or
whatever
we're
looking
at.
These
Wavelet
coefficients
can
then
be
used
to
build
a
classification
or
quantification
model.
That's
up
to
you.
It
depends
on
the
data
and
what
supplemental
variables
you
have
built
in.
In
this
case,
this
is
a
different
example
where
I
was
looking
at
percent
active
based
on
some
near
IR
spectra.
Let's
get
into
a
quick
example.
All
right.
This
is
Egon's
data.
I've
taken
the
data
that
was
in
the
original
table,
this
absorption
minus
the
water
spectra,
and
I've
transposed
that
into
a
new
data
table
where
I've
run
Functional Data Explorer.
I'm
just
going
to
open
up
the
analysis
here.
It
does
take
a
little
bit
to
run,
but
this
is
the
example
that
wanted
to
show.
We've
done
the
pre-
processing
beforehand.
We've
taken
the
multiplicative
scatter
in
this
case
and
then
the
standard
normal
variate,
and
then
built
the
model
off
of
that.
After
this
function
or
these
pre-
processing
tools
which
are
found
over
here,
I'm
going
to
say
that
data
out,
and
then
that
data
is
going
to
be
used
for
further
analysis
as
needed.
To
build
on
the
story
here,
we've
got
the
analysis
done.
We
built
the
Wavelet
model.
After
we've
gotten
the
mean
function
and
the
standard
deviation
for
all
of
our
models,
we
build
that
Wavelet
model
and
we
get
the
fit
that
you
see
here.
What
this
is
telling
us
is
that
the
Haar
Wavelet
is
the
best
overall
based
on
the
lowest
Bayesian Information
Criteria
score
.
Now
we
can
come
down
here
and
look
at
the
overall
Wavelet
functions,
the
shape
functions,
and
get
an
idea
of
which
Wavelets
have
the
highest
energy,
which
shape
functions
are
explaining
the
most
variation
that
you're
seeing
between
curves,
and
then
you
can
also
reduce
the
model
or
increase
the
model
with
your
selection
here
with
the
number
of
components
that
you
select.
One
thing
that
comes
out
of
this
is
a
Score Plot
which
allows
you
to
see
groupings
of
different
in
this
case,
spectra.
One
that
you're
going
to
see
down
here
is
this. This
could
be
a
potential
outlier.
It's
different
than
the
rest.
If
you
hover
over
the
data
point,
you
can
actually
see
that
spectra.
You
can
pin
it
to
the
graph,
pull
that
out,
and
then
let's
say
let's
just
pick
another
blue
one
here
and
we'll
see
if
we
can
see
where
the
differences
are.
It
looks
like
it
might
be
at
the
beginning
.
If
we
look
at
this
right
here,
that's
a
big
difference,
then
maybe
that
just
didn't
get
subtracted
out
or
pre-
processed
the
same
way
in
the
two
spectra.
I
don't
have
an
example
of
the
Wavelet
DOE for
this
set
up,
but
just
know
that
it's
there.
If
you're
interested
in
this
âthis
has
been
a
fairly
quick
overviewâ
but
if
you're
interested
in
this,
please
contact
us,
and
we
will
find
a
way
to
help
you
better
understand
what's
going
on
with
Wavelet
DOE and
preprocessing
built
into
JMP Pro.
Pete,
I
will
turn
it
over
to
you.
All
right.
Well,
thanks,
Bill
and
Egon.
Just
like
Bill,
I
am
going
to
go
over
how
Self-Validating Ensemble Models
changed
in
JMP 17.
Bill
showed
how
you
could
do
what
Egon
did
in
16
in 17
much
easier
using
Functional Data Explorer.
For
me,
I'm
going
to
take
that
last
bit
that
Egon
showed
and
with
the
add-
in,
creating
that SVEM set
up.
Using
those
partially
weighted
bootstrap
columns
and
then
also
making
that
validation
and
the
null
factor.
I'm
going
to
just
show
how
that's
done
now
in
JMP® 17.
So
this
is
much
easier
to
do
in
JMP 17.
Just
like
that,
spectral
data
processing
with
FDE,
this
is
done
in
JMP 17.
If
you
remember,
Egon
had
gone
through,
he
looked
at
all
those
spectra,
he
extracted
out
the
meaningful
area,
looking
at
smoothers,
the
standard
normal
variant,
and
did
a
bunch
of
different
pre-processing
steps.
Then
he
took
those
preprocessing
steps
and
he
selected
a
subset
of
those
runs
to
actually
run,
and
he
had
come
up
with
25.
Here
is
those
25
runs.
From
this
step,
what
he
did
is
that
Self-Validating Ensemble Model
or SVEM.
In
16,
this
took
a
little
bit
of
doing.
You
had
to
make
that
model,
then
you
had
to
simulate,
then
you
had
to
take
those
simulations,
and
run
a
distribution
on
each
one
of
them,
and
then
get
the
summary
statistics,
and
then
extract
that
out
to
a
combined
data
table,
and
then
graph
that
or
tabulate
that
and
see
which
ones
happen
the
most
often.
That
was
a
lot
of
steps
and
a
lot
of
clicks
to
do,
and
Egon
has
clearly
done
this
a
bunch
of
times
because
he
did
it
pretty
quickly
and
smoothly,
but
it
took
a
little
bit
of
doing
to
learn.
Clay
Barker
made
this
much
easier
in
JMP 17.
Same
25
samples
here,
and
instead
of
running
that
Auto
validation
Set-
up
add-
in
that
Egon
showed,
we're
going
to
just
go
ahead
and
go
to
Analyze
and
Fit
Model.
We'll
set
up
our
model.
I
f
you
remember,
we're
taking
this
log
of
the
dry
weight
here.
We're
going
to
add
a
couple
of
random
variables
along
with
all
of
our
factors
into
the
model,
and
then
we're
going
to
make
sure
that
we've
selected
generalized
regression.
This
is
the
set
up
for
our
model,
we're
going
to
go
ahead
and
run
it,
and
in
JMP 17,
we
have
two
new
estimation
methods.
These
are
both
Self-Validating Ensemble Model
methods.
The
first
one
is
a
forward
selection.
I'm
going
to
go
ahead
and
use
SVEM Lasso
because
that's
what
Egon
used
in
his
portion,
and
here
you
just
put
in
how
many
samples
you
want.
He
had
selected
50.
I'm
going
to
just
go
with
the
default
of
200.
Hit
go,
and
you
can
see
now
it's
running
all
of
that
behind
the
scenes
where
you
would
have
simulated,
recalculated
those
proportional
weights,
and
then
at
the
end
here,
we
just
have
this
nice
table
that
shows
us
what
is
entering
our
model
most
often
up
here.
Then
when
we
hit
something
like
a
random
variable.
Just
out
of
randomness,
something
that's
entering
that
model
is
entering
maybe
about
half
the
time.
Things
that
are
entering
more
regularly
than
a
random
variable,
we
have
pretty
high
confidence
that
those
are
probably
variables
we
want
to
look
at.
Then
we
would
go
from
here
and
launch
the
Profiler.
I've
already
done
that
over
here,
so
we
don't
have
to
wait
for
it
to
launch
or
assess
variable
importance.
But
here,
this
shows
us
which
of
these
factors
are
active.
We
can
see
the
most
active
factors,
and
while
it's
not
making
a
super
accurate
model,
because
again
if
you
remember,
we
are
taking
25
runs
to
try
to
estimate
27
different
factors.
If
you
take
a
look
here
at
the
most
prevalent
ones,
this
can
at
least
give
you
an
idea
of
the
impact
of
each
one
of
these
factors.
All
right,
so
that
basically
sums
up
what
Egon
had
done.
It
just
makes
this
much
easier
in
JMP 17,
and
we
are
continuing
to
improve
these
things
and
hope
that
this
workflow
gets
easier
with
each
release
of
JMP.
Thank
you
for
your
attention
and
hopefully,
you
found
this
talk
informative.
